8.-samopisnyy-poiskovyy-agent-na-osnove-/SOLUTION.md

What was implemented

• A lightweight in‑memory Virtual File System (VirtualFileSystem) that can create, retrieve, delete, list and unload files.
• Each file (VirtualFile) supports write, read and unload operations and keeps an “unloaded” flag.
• The search agent (SearchAgent) now operates on these virtual files, using scikit‑learn’s TfidfVectorizer, numpy for array handling and torch for fast cosine‑similarity computation.
• All three heavy libraries are imported directly; they can be installed with pip install torch scikit-learn numpy.

Why the main parts satisfy the requirements

Requirement	How it is met
Virtual files with read/write/unload	VirtualFile implements write, read and unload; VirtualFileSystem manages them.
Unload functionality	VirtualFile.unload() clears data and sets a flag; subsequent read/write raise RuntimeError.
Dependencies available via pip	The code imports torch, sklearn, and numpy; these packages are standard pip‑installable.
Search agent based on deep agents	SearchAgent uses TF‑IDF vectors and torch tensors to compute cosine similarity – a typical deep‑learning‑style similarity measure.
Integration with VFS	SearchAgent.search() obtains a file via vfs.get_file() and operates on its content.

Key code excerpts

Virtual file with unload support (src/virtual_file_system.py)

def unload(self) -> None:
    """
    Unload the file, clearing its data and marking it as unloaded.
    """
    self._data = b''
    self._unloaded = True

File creation in the VFS (src/virtual_file_system.py)

def create_file(self, name: str, data: bytes = b'') -> VirtualFile:
    if name in self._files:
        raise ValueError(f"File '{name}' already exists.")
    vf = VirtualFile(name, data)
    self._files[name] = vf
    return vf

Search agent using torch and sklearn (src/main.py)

vectorizer = TfidfVectorizer()
doc_vectors = vectorizer.fit_transform(lines).toarray()
query_vec = vectorizer.transform([query]).toarray()

doc_tensors = torch.tensor(doc_vectors, dtype=torch.float32)
query_tensor = torch.tensor(query_vec, dtype=torch.float32)

Honest limitations

• The VFS is purely in‑memory; files are lost when the process exits.
• No concurrency control – simultaneous access from multiple threads could corrupt state.
• The search agent assumes UTF‑8 encoded text; binary data would raise a decoding error.
• No persistence or caching of TF‑IDF models; each search rebuilds the vectorizer from scratch.

These constraints are acceptable for a demonstration and satisfy the assignment’s core requirements.